> ## Documentation Index
> Fetch the complete documentation index at: https://docs.promptguard.co/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG with PII Redaction

> Build a secure RAG pipeline that automatically redacts sensitive data

This example shows how to build a Retrieval-Augmented Generation (RAG) system that automatically redacts PII from both user queries and retrieved documents before sending to the LLM.

## Overview

RAG systems often process sensitive documents (HR records, customer data, legal contracts). This example demonstrates:

* Auto-redacting PII from user queries
* Sanitizing retrieved documents before LLM context
* Maintaining answer quality while protecting data

## Architecture

```mermaid theme={"system"}
graph LR
    A[User Query] --> B[PromptGuard]
    B -->|Redact PII| C[Vector Search]
    C --> D[Retrieved Docs]
    D --> E[PromptGuard]
    E -->|Redact PII| F[LLM]
    F --> G[Response]
    G --> B
    B -->|Redact PII| H[User]
```

## Implementation

### Setup

```bash theme={"system"}
pip install promptguard-sdk openai chromadb
```

### Basic RAG with Protection

```python theme={"system"}
import promptguard
from openai import OpenAI
import chromadb

# Initialize PromptGuard with response scanning
promptguard.init(
    api_key="pg_live_xxxxxxxx",
    mode="enforce",
    scan_responses=True,
)

# Initialize clients
openai_client = OpenAI()
chroma_client = chromadb.Client()
collection = chroma_client.get_or_create_collection("documents")

def secure_rag_query(user_query: str) -> str:
    # Step 1: User query is auto-scanned by promptguard.init()

    # Step 2: Retrieve relevant documents
    results = collection.query(
        query_texts=[user_query],
        n_results=5
    )

    # Step 3: Build context from retrieved docs
    context = "\n\n".join(results["documents"][0])

    # Step 4: LLM call - auto-scanned by PromptGuard
    response = openai_client.chat.completions.create(
        model="gpt-5-nano",
        messages=[
            {
                "role": "system",
                "content": f"Answer based on this context:\n\n{context}"
            },
            {
                "role": "user",
                "content": user_query
            }
        ]
    )

    return response.choices[0].message.content

# Example usage
answer = secure_rag_query("What's the salary for employee John Smith?")
print(answer)
```

### With LangChain

```python theme={"system"}
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.chains import RetrievalQA
from promptguard.integrations.langchain import PromptGuardCallbackHandler

pg_handler = PromptGuardCallbackHandler(
    api_key="pg_live_xxxxxxxx",
    scan_responses=True,
)

llm = ChatOpenAI(model="gpt-5-nano", callbacks=[pg_handler])
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
)

answer = qa_chain.invoke("What benefits does Jane Doe have?")
```

## PII Types Detected

| Type        | Example                                     | Redacted As     |
| ----------- | ------------------------------------------- | --------------- |
| Names       | John Smith                                  | \[PERSON]       |
| Email       | [john@company.com](mailto:john@company.com) | \[EMAIL]        |
| Phone       | (555) 123-4567                              | \[PHONE]        |
| SSN         | 123-45-6789                                 | \[SSN]          |
| Credit Card | 4532-1234-5678-9012                         | \[CREDIT\_CARD] |

## Next Steps

<CardGroup cols={2}>
  <Card title="Data Privacy Example" icon="eye-slash" href="/cookbooks/data-privacy">
    More data protection patterns
  </Card>

  <Card title="Threat Detection" icon="shield" href="/security/threat-detection">
    Understand detection capabilities
  </Card>
</CardGroup>